Asparuh KoevPresident & CEO

Logistics is a highly unpredictable environment. Data quality is often poor, information changes constantly, and unexpected events happen every day. Unlike software development, where AI operates in a controlled environment, logistics requires handling ambiguity and exceptions that AI still struggles to manage reliably.

When AI writes code, it has access to complete information and immediate feedback. In logistics, information is often incomplete, inaccurate, or outdated. A freight listing may no longer be available, a shipment may change unexpectedly, or operational conditions may shift rapidly, making decisions much more complex.

Many businesses want to fully automate processes too quickly. AI should be used as a tool that supports people rather than replacing human decision-makers. The most successful implementations today combine AI's analytical capabilities with human judgment and accountability.

AI is particularly valuable at identifying opportunities and exceptions that humans might miss. It can analyze thousands of freight opportunities, evaluate route options, monitor operational data, and highlight the most relevant information, allowing employees to focus their attention more effectively.

AI can process enormous amounts of data and identify optimal solutions much faster than people can. For example, it can analyze driver schedules, vehicle locations, delivery commitments, and available freight to recommend the best dispatch decisions while leaving final approval to human operators.

Accountability matters. If an AI system makes a mistake, businesses still need someone responsible for the outcome. Human oversight helps ensure decisions are reviewed properly and provides a safeguard against errors that AI systems may not recognize on their own.

AI can monitor camera feeds and identify risky behaviors such as unsafe braking, lane departures, or other driving incidents. It helps surface important events quickly, but people should still review those events and determine the appropriate response rather than relying entirely on automation.

AI must create measurable business value. Companies should focus on areas where AI can generate additional revenue, improve efficiency, reduce costs, or optimize resource utilization. If the technology does not produce clear business benefits, it becomes difficult to justify the investment.

Commercial activities such as pricing, quoting, customer engagement, and freight selection have a direct connection to revenue. When AI helps improve decision-making in these areas, companies can often measure the financial impact more clearly than in many other operational functions.

AI systems increasingly influence how work is assigned and how employees operate. Decisions about routes, schedules, loads, and driver utilization can significantly impact people's lives. Companies need to ensure these systems are transparent, fair, and aligned with human interests.

AI can influence where drivers go, how long they stay on the road, and what assignments they receive. Without proper oversight, automated systems could optimize purely for efficiency while overlooking the human impact on drivers and their quality of life.

Responsible adoption starts with clear objectives. Businesses should use AI to improve outcomes for customers, employees, and operations rather than focusing only on automation. The technology should support human decision-making and create benefits across the entire organization.

Potentially, yes. If AI helps improve scheduling, reduce inefficiencies, and create better working conditions, it can make driving careers more attractive. Improving the overall experience for drivers may help address workforce shortages over time.